Zurich Insurance Uses Data Analytics to Leverage the BI Insurance Proposition
Kamil J. Mizgier (),
Otto Kocsis () and
Stephan M. Wagner ()
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Kamil J. Mizgier: Department of Management, Technology, and Economics, Swiss Federal Institute of Technology Zurich, 8092 Zurich, Switzerland
Otto Kocsis: Zurich Insurance Company Ltd., 8045 Zurich, Switzerland
Stephan M. Wagner: Department of Management, Technology, and Economics, Swiss Federal Institute of Technology Zurich, 8092 Zurich, Switzerland
Interfaces, 2018, vol. 48, issue 2, 94-107
Abstract:
As the interdependencies due to global trade and interconnected value chains have grown, firms and their value chains have become more prone to disruptions. Consequently, many firms resort to business interruption (BI) insurance to transfer the disruption risk. Given the limited amount of literature available about BI loss and claims characteristics, insurance companies and their customers will benefit from the insights that resulted from the project underlying this study. The project involved a collaboration between Zurich Insurance and the Swiss Federal Institute of Technology Zurich, in which we extracted a large amount of data pertaining to BI claims from various data sources and analyzed these data. We found, for example, that the average share of BI losses has increased significantly over the past 15 years. Moreover, the BI risk exposure, measured as BI share of the total insurance claims, the average recovery time, and the increased costs of working, differs significantly based on the industry affiliation of the firm experiencing the loss. Our results have implications for the targeted risk assessment of BI risk exposures and the development of tailored supply chain risk management practices and risk transfer along the value chain.
Keywords: decision analysis; risk; financial institutions; insurance; business interruption insurance; claims analysis; empirical research; data analytics (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (4)
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Persistent link: https://EconPapers.repec.org/RePEc:inm:orinte:v:48:y:2018:i:2:p:94-107
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